Penerapan Model Vector Autoregressive Integrate Moving Average dalam Peramalan Laju Inflasi dan Suku Bunga di Indonesia

Jusmawati Jusmawati, Mustika Hadijati, Nurul Fitriyani

Abstract


The inflation and interest rates in Indonesia have a significant impact on the country's economic development. Indonesian inflation and interest rates data are multivariate time series data that show activity over a certain period of time. Vector Autoregressive Integrated Moving Average (VARIMA) is a method for analyzing multivariate time series data. This method is a simultaneous equation modeling that has several endogenous variables simultaneously. This study aimed to model the inflation and interest rates data, from January 2009 to December 2016 and predict inflation and interest rates by using VARIMA method. The model obtained was the VARIMA(0,2,2) model, with estimated parameters using the maximum likelihood method. The choice of the VARIMA(0,2,2) model was based on the smallest AIC value of -4,2891, with a MAPE value for the inflation and interest rates forecasting were 6,04% and 1,84%, respectively, which indicates a very good forecast results.

Keywords


Akaike’s Information Criterion (AIC); Mean Absolute Percentage Error (MAPE); Multivariate time series; VARIMA

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References


Bain L.J.dan Engelhardt, M.(1992). Introduction to Probability and Mathematical Statistics. California: Duxbury Press.

Bank Indonesia (BI). (2016).Informasi Dasar Suku Bunga. http://www.bi.go.id/. Diakses pada Januari 2019.

Draper, N. R. dan Smith, H. (1998)Applied Regression Analysis 3rdEdition. United States of America: John Wiley and Sons.

Hermayani, Nohe, D.A., dan Fathurahman, M. (2014). Mengatasi Heteroskedastisitas pada Model ARIMA Menggunakan ARCH-GARCH (Studi Kasus: IHK Provinsi Kalimantan Timur Tahun 2005-2012).Jurnal Eksponensial, 5(1), 73 – 81.

Makridakis, S., Wheelwright, S., dan McGee, V. (1999). Metode dan Aplikasi Peramalan, Jilid 1 Edisi Kedua Terjemahan Ir. Untung S. Andriyanto dan Ir. Abdul Basith.Jakarta: Penerbit Erlangga.

Mankiw, N. G. (2007). Teori Makroekonomi Edisi Keenam. Jakarta: Erlangga.

Matjik,A.A. danSumertajaya, I. M. (2011).Sidik Variabel Ganda dengan Menggunakan SAS. Bogor: IPB PRESS.

Rinaldy, R. (2016). Analisis Peramalan Data Runtun Waktu Menggunakan Vector Autoregressive Integrated Moving Average (VARIMA), SkripsiJurusan Matematika Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Lampung.

Sukirno, S. (2008). Teori Pengantar Ekonomi Makro.Jakarta: Raja Grafindo Persada.

Sunariyah. (2014). Pengantar Pengetahuan Pasar Modal Edisi Kelima.Bandung: CV Alfabeta.

Wei, W. W. (2006). Time Series Analysis: Univariate and Multivariate Method.USA: Pearson Educations.

Zainun, N.Y. dan Majid,M.Z.A. (2003). Low Cost House Demand Predictor.Malaysia: Universitas Teknologi Malaysia.




DOI: https://doi.org/10.29303/emj.v3i2.62

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